Interaction with one or more datasets is the basis for many applications, both in the military and the civilian realm. Many of these datasets are multidimensional, having data that represent values in more than one dimension.
For example, a dataset of ocean water characteristics can include three-dimensional data of latitude, longitude, and water depth, where every data point represents a specific location in the ocean as defined by particular latitude, longitude, and depth. Other data regarding the ocean's chemical and physical properties such as salinity, temperature, current speed, and direction also can be part of the dataset. In addition, such a dataset can also have an additional dimension such as time, wherein a particular point in the dataset represents a value for a specific location in the ocean at a particular time.
Working with data in a multidimensional dataset is a key part of the planning, development, and execution of operations, both civilian and military, relating to that data. For example, with respect to an ocean water dataset mentioned above, success of an operation can require careful analysis of many chemical and physical conditions of the water that can affect the operation's outcome. One important chemical property is salinity of the water at a particular point, which can be used with temperature and pressure (depth) to calculate water density, which in turn can determine the buoyant force range that an underwater vessel should be calibrated to in order to operate within the environment without sinking or becoming unrecoverable. Important physical characteristics can include water current speed and direction, which must also be known to be within an acceptable range for the operational limitations of the vehicle in question. Current speed and direction can also be used to calculate estimates of energy consumption and estimated time of arrival for points along a proposed operation route.
It is easy to see that this information is important to civilian operations such as commercial shipping and navigation, offshore oil and gas exploration, coastal management, and commercial fishing. Moreover, this information is also crucial to military missions such as operation of conventional Navy vessels and navigation equipment, missions relating to underwater gliders and unmanned underwater vehicles, and manned missions such as those conducted by Navy SEALs.
When planning for missions that consider these and other characteristics, it is beneficial to analyze all of the applicable datasets for the same volume at the same time, for example, to determine whether they have independent or combined effects on the outcome. One way of analyzing datasets is to graphically render the data, for example, into tables, charts or other visual displays. However, graphically rendering multiple-dimension datasets and allowing the user to navigate freely through them is extremely computer-intensive and complex to implement. See D. Hearn and M. P. Baker, “Computer Graphics C version,” Second Edition, 1997. The advantages of this type of user interaction with the data in terms of added user decision-making ability in light of the required computing resources often are questionable at best. In addition, in some environments there may not be a horizon or other physical object to use as a frame of reference for proper orientation. This problem is particularly acute in underwater environments, where users can become disoriented and lose directional context regarding what they are viewing.
In addition, such datasets often are extremely large and therefore difficult and computer resource intensive to navigate due to the sheer size of the data involved.
To address some of these problems, one alternative to rendering graphics for multiple data sets is creation of a single union of the combined effects of all the data by applying thresholds to values within and in between each dataset to evaluate the overall conditions for the environment. One such approach is often referred to as “traffic light analysis” (TLA) since the results are usually limited to values that correspond to green (go), yellow (wait), or red (no go). See B. Bourgeois et al., “Undersea Mission Planning Visualization Support for Deconfliction of 4D×N Constraints,” 15th International Symposium on Unmanned Untethered Submersible Technology 07, Durham, N.H., August 2007. Further processing of these results into Geospatial Bitmaps provides the added benefits of vector-to-raster conversion routines and high performance data comparisons between datasets. See U.S. Pat. No. 6,218,965, Moving Map Composer (2001). However, as noted above, graphically rendering multiple-dimension datasets is extremely computer-intensive and complex to implement, and thus may not be feasible in many cases.
Thus, it is desirable to provide a user control to enable a user to better work with multidimensional datasets. In particular, it often is desirable to provide a user a means to select a subset of the data so that relevant data may be analyzed to provide more useful results with less load on computer resources.